Gregory Cooper, MD, PhD

Lung Cancer Survival Prediction Using Instance-Specific Bayesian Networks

Fattaneh Jabbari, Lisa C. Villaruz, Mike Davis, Gregory F. Cooper.  Lung Cancer Survival Prediction Using Instance-Specific Bayesian Networks. In: Michalowski M., Moskovitch R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science, vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_14.

On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge

Andrews B, Spirtes P, Cooper GF.  On the Completeness of Causal Discovery in the Presence of Latent Confounding with Tiered Background Knowledge.  Proceedings of Machine Learning Research JMLR:108.

Leveraging eye tracking to prioritize relevant medical record data: Comparative Machine Learning Study

King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran S
Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study
J Med Internet Res 2020;22(4):e15876 

Learning Latent Causal Structures with a Redundant Input Neural Network

Young JD, Andrews B, Cooper GF, Lu X. Learning Latent Causal Structures with a Redundant Input Neural Network. arXiv:2003.13135v1 29 Mar 2020.

 

A Bayesian approach for detecting a disease that is not being modeled

Aronis JM, Ferraro JP, Gesteland PH, Tsui F, Ye Y, Wagner MM, Cooper GF. A Bayesian approach for detecting a disease that is not being modeled. PLOSOne. 2020 Feb 28;15(2):e0229658. doi: 10.1371

Exploring novel graphical presentations of clinical data in a Learning Electronic Medical Record

Luca Calzoni, Gilles Clermont, Gregory F. Cooper, Shyam Visweswaran, Harry Hochheiser.  Exploring novel graphical presentations of clinical data in a Learning Electronic Medical Record. Appl Clin Inform 2020; 11(04): 680-691. DOI: 10.1055/s-0040-1709707. PMID: 33058103

Learning Adjustment Sets from Observational and Limited Experimental Data

Sofia Triantafillou, Gregory F. Cooper. Learning Adjustment Sets from Observational and Limited Experimental Data. 2020 . arXiv preprint arXiv:2005.08749, 2020

Tumor-specific causal inference discovers distinct disease mechanisms underlying cancer subtypes

Xue Y, Cooper GF, Cai C, Lu S, Hu B, Ma X, Lu X. Tumor-specific causal inference discovers distinct disease mechanisms underlying cancer subtypes. Scientific Reports, (2019) doi:10.1038/s41598-019-48318-7

Learning high-dimensional directed acyclic graphs with mixed data-types

Andrews B, Ramsey J, Cooper GF.  Learning high-dimensional directed acyclic graphs with mixed data-types. Proceedings of Machine Learning Research 2019 Aug; 104: 4-21. PMID: 31453569 PMCID: PMC6709674

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference

Cai C, Cooper GF, Lu KN, Ma X, Xu S, Zhao Z, Chen X, Xue Y, Lee AV, Clark N, Chen V, Lu S, Chen L, Hochheiser HS, Jiang X, Wang QJ, Lu X.  Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference. PLoS Computational Biology. Published online July 5, 2019. doi: 10.137/journal,pcbi.1007088.  PMID: 31276486

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